Deep learning methods used in modern machine learning have particularly excelled at detecting complex structures in high-dimensional, complex data. The use of deep learning in early identification and automatic categorization of Alzheimer's disease has garnered significant interest, thanks to sophisticated neuroimaging methods that generate multimodal neuroimaging data. This has enabled researchers to develop more precise tools for diagnosis and monitoring. Alzheimer's disease typically affects those 60 years or older. But doctors are now diagnosing more middle-aged cases, specifically those aged 45-65. Research shows that this group is at greater risk. Early diagnosis and treatment can slow progression and improve quality of life. We advise adopting the Region-based Convolutional Neural Network (RCNN) approach to categorize features from intricate medical images. It is frequently used in medical image analysis and increases feature extraction accuracy. We examine the efficacy of feature extraction and feature selection in enhancing performance and producing accurate results, which are crucial components in classification. The strategy achieves comparable results to analyzing all data at once but reduces the number and cost of biomarkers needed for diagnosis. This is done by selectively identifying and using relevant biomarkers, simplifying the diagnostic process, and reducing costs. As a result, it might help with the accurate and personalized detection of AD and be useful in clinical situations.

#